Real-time comparison of quality of interfaces

ABSTRACT

In some embodiments, a system and method for substantially real-time comparison of quality of interfaces by mobile devices over heterogeneous networks is disclosed. The method can be performed using a dynamic and rapid comparison by distributed hosts, using a minimal number of injected network packets, using minimal path quality metrics, which path quality metrics are independent of how a QoI is measured, and in a manner suitable for both wireline and wireless networks.

The present application claims priority under 35 U.S.C. 119 to U.S.Provisional Application Ser. No. 60/662,749 filed on Mar. 17, 2005,entitled Real-Time Comparison of Quality of Interfaces (QoI), by Van DenBerg, et al., the entire disclosure of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present application relates to communications over computernetworks. The preferred embodiments also relate more particularly tosystems and methods for comparing the qualities of interfaces and, mostparticularly, to real-time comparison of quality of interfaces by mobiledevices over heterogeneous radio networks.

BACKGROUND Networks and Internet Protocol

There are many types of computer networks, with the Internet having themost notoriety. The Internet is a worldwide network of computernetworks. Today, the Internet is a public and self-sustaining networkthat is available to many millions of users. The Internet uses a set ofcommunication protocols called TCP/IP (i.e., Transmission ControlProtocol/Internet Protocol) to connect hosts. The Internet has acommunications infrastructure known as the Internet backbone. Access tothe Internet backbone is largely controlled by Internet ServiceProviders (ISPs) that resell access to corporations and individuals.

With respect to IP (Internet Protocol), this is a protocol by which datacan be sent from one device (e.g., a phone, a PDA [Personal DigitalAssistant], a computer, etc.) to another device on a network. There area variety of versions of IP today, including, e.g.,

IPv4, IPv6, etc. Each host device on the network has at least one IPaddress that is its own unique identifier.

IP is a connectionless protocol. The connection between end pointsduring a communication is not continuous. When a user sends or receivesdata or messages, the data or messages are divided into components knownas packets. Every packet is treated as an independent unit of data.

In order to standardize the transmission between points over theInternet or the like networks, an OSI (Open Systems Interconnection)model was established. The OSI model separates the communicationsprocesses between two points in a network into seven stacked layers,with each layer adding its own set of functions. Each device handles amessage so that there is a downward flow through each layer at a sendingend point and an upward flow through the layers at a receiving endpoint. The programming and/or hardware that provides the seven layers offunction is typically a combination of device operating systems,application software, TCP/IP and/or other transport and networkprotocols, and other software and hardware.

Wireless Networks

Wireless networks can incorporate a variety of types of mobile devices,such as, e.g., cellular and wireless telephones, PCs (personalcomputers), laptop computers, wearable computers, cordless phones,pagers, headsets, printers, PDAs, etc. For example, mobile devices mayinclude digital systems to secure fast wireless transmissions of voiceand/or data. Typical mobile devices include some or all of the followingcomponents: a transceiver (i.e., a transmitter and a receiver,including, e.g., a single chip transceiver with an integratedtransmitter, receiver and, if desired, other functions); an antenna; aprocessor; one or more audio transducers (for example, a speaker or amicrophone as in devices for audio communications); electromagnetic datastorage (such as, e.g., ROM, RAM, digital data storage, etc., such as indevices where data processing is provided); memory; flash memory; a fullchip set or integrated circuit; interfaces (such as, e.g., USB, CODEC,UART, PCM, etc.); and/or the like.

Wireless LANs (WLANs) in which a mobile user can connect to a local areanetwork (LAN) through a wireless connection may be employed for wirelesscommunications. Wireless communications can include, e.g.,communications that propagate via electromagnetic waves, such as light,infrared, radio, microwave. There are a variety of WLAN standards thatcurrently exist, such as, e.g., Bluetooth, IEEE 802.11, and HomeRF.

By way of example, Bluetooth products may be used to provide linksbetween mobile computers, mobile phones, portable handheld devices,personal digital assistants (PDAs), and other mobile devices andconnectivity to the Internet. Bluetooth is a computing andtelecommunications industry specification that details how mobiledevices can easily interconnect with each other and with non-mobiledevices using a short-range wireless connection. Bluetooth creates adigital wireless protocol to address end-user problems arising from theproliferation of various mobile devices that need to keep datasynchronized and consistent from one device to another, thereby allowingequipment from different vendors to work seamlessly together. Bluetoothdevices may be named according to a common naming concept. For example,a Bluetooth device may possess a Bluetooth Device Name (BDN) or a nameassociated with a unique Bluetooth Device Address (BDA). Bluetoothdevices may also participate in an Internet Protocol (IP) network. If aBluetooth device functions on an IP network, it may be provided with anIP address and an IP (network) name. Thus, a Bluetooth Device configuredto participate on an IP network may contain, e.g., a BDN, a BDA, an IPaddress and an IP name. The term “IP name” refers to a namecorresponding to an IP address of an interface.

An IEEE standard, IEEE 802.11, specifies technologies for wireless LANsand devices. Using 802.11, wireless networking may be accomplished witheach single base station supporting several devices. In some examples,devices may come pre-equipped with wireless hardware or a user mayinstall a separate piece of hardware, such as a card, that may includean antenna. By way of example, devices used in 802.11 typically includethree notable elements, whether or not the device is an access point(AP), a mobile station (STA), a bridge, a PCMCIA card or another device:a radio transceiver; an antenna; and a MAC (Media Access Control) layerthat controls packet flow between points in a network.

In addition, Multiple Interface Devices (MIDs) may be utilized in somewireless networks. MIDs may contain two independent network interfaces,such as a Bluetooth interface and an 802.11 interface, thus allowing theMID to participate on two separate networks as well as to interface withBluetooth devices. The MID may have an IP address and a common IP(network) name associated with the IP address.

Wireless network devices may include, but are not limited to Bluetoothdevices, Multiple Interface Devices (MIDs), 802.11x devices (IEEE 802.11devices including, e.g., 802.11a, 802.11b and 802.11g devices), HomeRF(Home Radio Frequency) devices, Wi-Fi (Wireless Fidelity) devices, GPRS(General Packet Radio Service) devices, 3G cellular devices, 2.5Gcellular devices, GSM (Global System for Mobile Communications) devices,EDGE (Enhanced Data for GSM Evolution) devices, TDMA type (Time DivisionMultiple Access) devices, or CDMA type (Code Division Multiple Access)devices, including CDMA2000. Each network device may contain addressesof varying types including but not limited to an IP address, a BluetoothDevice Address, a Bluetooth Common Name, a Bluetooth IP address, aBluetooth IP Common Name, an 802.11 IP Address, an 802.11 IP commonName, or an IEEE MAC address.

Wireless networks can also involve methods and protocols found in, e.g.,Mobile IP (Internet Protocol) systems, in PCS systems, and in othermobile network systems. With respect to Mobile IP, this involves astandard communications protocol created by the Internet EngineeringTask Force (IETF). With Mobile IP, mobile device users can move acrossnetworks while maintaining their IP Address assigned once. See Requestfor Comments (RFC) 3344. NB: RFCs are formal documents of the InternetEngineering Task Force (IETF). Mobile IP enhances Internet Protocol (IP)and adds means to forward Internet traffic to mobile devices whenconnecting outside their home network. Mobile IP assigns each mobilenode a home address on its home network and a care-of-address (CoA) thatidentifies the current location of the device within a network and itssubnets. When a device is moved to a different network, it receives anew care-of address. A mobility agent on the home network can associateeach home address with its care-of address. The mobile node can send thehome agent a binding update each time it changes its care-of addressusing, e.g., Internet Control Message Protocol (ICMP).

In basic IP routing (i.e. outside mobile IP), typically, routingmechanisms rely on the assumptions that each network node always has aconstant attachment point to, e.g., the Internet and that each node's IPaddress identifies the network link it is attached to. In this document,the terminology “node” includes a connection point, which can include,e.g., a redistribution point or an end point for data transmissions, andwhich can recognize, process and/or forward communications to othernodes. For example, Internet routers can look at, e.g., an IP addressprefix or the like identifying a device's network. Then, at a networklevel, routers can look at, e.g., a set of bits identifying a particularsubnet. Then, at a subnet level, routers can look at, e.g., a set ofbits identifying a particular device. With typical mobile IPcommunications, if a user disconnects a mobile device from, e.g., theInternet and tries to reconnect it at a new subnet, then the device hasto be reconfigured with a new IP address, a proper netmask and a defaultrouter. Otherwise, routing protocols would not be able to deliver thepackets properly.

Quality of Interfaces

A number of approaches are available that compare the qualities of theinterfaces based on a very narrow set of criteria such as radio signalstrength or signal to noise ratios. However, these approaches limittheir comparison to the radio network.

Using end-to-end path quality comparisons to select optimal paths hasbeen studied in Reference [6], incorporated herein below, in the contextof MPLS (Multi-protocol Labal Switching) networks, where a centralizeddecision is made on optimal routes to connect traffic sources anddestinations. Furthermore, the end-to-end path quality comparisons areused within a single autonomous domain, where the network operatortypically has access to all the required statistics. However, thesestatistics are hard to estimate for an individual mobile. Therefore,such methods are not suitable for an individual multi-interface (mobile)host to compare the qualities of its interfaces in real time.

In addition, techniques are currently available for estimating theavailable bandwidth, delay or jitter along a network path and may beused to estimate these parameters for multiple interfaces and thencompare them (see Reference [10] incorporated below). However, thesetechniques typically require the device to send a large number of probepackets resulting in long delays and significant waste of scarcewireless network resources; hence, they are not suitable for wirelessdevices.

The following background references show, among other things, somebackground technologies related to comparing qualities of interfaces,all of which references are incorporated herein by reference in theirentireties as though recited herein in full.

-   [1] V. J. Ribeiro et al. “pathChirp: Efficient Available Bandwidth    Estimation for Network Paths”, PAM Workshop, 2003.-   [2] M. Jain, C. Dovrolis, “End-to-End available bandwidth:    measurement methodology, dynamics, and relation with TCP    throughput,” Proceedings of ACM SIGCOMM, 2002.-   [3] B. Melander, M. Bjorkman, P. Guningberg, “A new end-to-end    probing and analysis method for estimating bandwidth bottlenecks,”    Global Internet Symposium, 2000.-   [4] K. Lai, M. Baker, “Measuring link bandwidth using a    deterministic model of packet delay”, ACM SIGCOMM, August 2000.-   [5] N. Hu, P. Steenkiste, “Evaluation and Characterization of    Available Bandwidth Probing Techniques”, IEEE Journal of Selected    Areas in Comm., Vol. 21, No. 6, August 2003, pp. 879-894.-   [6] T. Anjani et al., “A New Path Selection Algorithm for MPLS    Networks Based on Available Bandwidth Estimation”, QofIS/ICQT 2002,    LNCS 2511, pp. 205-214, 2002.-   [7] T. Anjani et al., “ABEst: An Available Bandwidth Estimator    within an Autonomous System.”, Proceedings Globecom 2002.-   [8] B. K. Gosh, P. K. Sen, “Handbook of Sequential Analysis”, Marcel    Dekker, NY, 1991.-   [9] H. R. Neave, P. L. Worthington, “Distribution Free Tests”, Unwin    Hyman, London, 1988.-   [10] R. S. Prasad, M. Murray, C. Drovolis and K. Claffy, “Bandwidth    estimation: metrics, measurement techniques, and tools”, IEEE    Network, 2004.-   [11] S. Saroiu, P. K. Gummadi, S. D. Gribble, “SProbe: A Fast    Technique ‘for Measuring Bottleneck Bandwidth in Uncooperative    Environments”, Proceedings IEEE Infocom, 2002.-   [12] H. Kaaranen et al. “UMTS Networks: Architecture, Mobility and    Services”, Wiley, 2001.

V. K. Garg, “IS-95 CDMA and cdma2000: Cellular/PCS SystemsImplementation”, Prentice-Hall, 2000.

While a variety of communication systems and methods are known, thereremains a need for improved and enhanced systems and methods forcommunicating over the Internet and/or other networks. Among otherthings, while there has been work done in the area of physicallytracking devices and people, existing systems do not allow, inter alia,messages to be directed to particular locations and not to otherlocations.

SUMMARY OF THE PREFERRED EMBODIMENTS

The preferred embodiments of the present invention can significantlyimprove upon existing methods and/or apparatuses.

As discussed above, a number of approaches are available that comparethe qualities of the interfaces based on a very narrow set of criteriasuch as radio signal strength or signal to noise ratios, whichapproaches limit their comparison to the radio network. On the otherhand, in the preferred embodiments of the present invention, approachesare designed to handle many more criteria of interface qualities, takinga full end-to-end network path into account—including, by way ofexample, round-trip available bandwidth, end-to-end delay and delayjitter, unidirectional available bandwidth, delay and delay jitter alongan end-to-end network path through each interface.

Other methods are either 1) not suitable for dynamic, rapid comparisonby distributed hosts or 2) not applicable to general comparison metricsfor path. According to the preferred embodiments, techniques areprovided for a device to make rapid comparisons of the quality of theinterfaces using a minimal number of injected network packets, requiringminimal information regarding path quality metrics, and making itsuitable for both wireline and wireless networks.

According to the preferred embodiments, methods employed can achieve thecomparison with a very small number of probe packets and can be done ina much shorter time, hence it is well suited for wireless networks andfor situations where comparison needs to be done quickly (e.g., when QoIcomparison is used as the basis for determine when to handoff into a newnetwork).

In the preferred embodiments, techniques are provided for amulti-interface device to dynamically and rapidly compare the qualitiesof its interfaces in a domain of wireline and/or wireless paths inreal-time, and with minimal injection of network traffic. Thesetechniques preferably need minimal information about path qualitymetrics.

According to some embodiments, a method for substantially real-timecomparison of quality of interfaces (QoIs) by mobile devices overheterogeneous wireless networks, comprising: comparing in substantiallyreal-time the qualities of multiple interfaces using path qualitymetrics that are independent of how the QoI is measured, whethermeasured by a path through a wireless network alone or a path throughboth a wireless network and through a wired network, said methodincluding comparing path quality as a quickest change detection problemfor observations from a new interface or comparing path quality based onsequential two sample tests.

According to other embodiments, a method for substantially real-timecomparison of quality of interfaces (QoIs) by mobile devices overheterogeneous wireless networks, comprising: comparing in substantiallyreal-time the qualities of multiple interfaces using path qualitymetrics that are independent of how the QoI is measured, whethermeasured by a path through a wireless network alone or a path throughboth a wireless network and through a wired network, said methodincluding performing a one-sample change point detection method orperforming a non-parametric and on-line two-sample method.

According to other embodiments, a method for substantially real-timecomparison of quality of interfaces (QoIs) by mobile devices overheterogeneous wireless networks, comprising: comparing in substantiallyreal-time the qualities of multiple interfaces using path qualitymetrics that are independent of how the QoI is measured, includingcomparing path quality as a quickest change detection problem forobservations from the new interface. In some examples, there are a setof observations from a new interface, and said comparing includesdetecting whether the mean of these observations will be higher than anaverage available metric value. In some examples, the method includesemploying a cumulative sum procedure for a path quality comparison.

According to other embodiments, a method for substantially real-timecomparison of quality of interfaces (QoIs) by mobile devices overheterogeneous wireless networks, comprising: comparing in substantiallyreal-time the qualities of multiple interfaces using path qualitymetrics that are independent of how a QoI is measured, includingcomparing path qualities based on sequential two sample tests.

According to other embodiments, a method for substantially real-timecomparison of quality-of-interfaces by mobile devices over heterogeneousradio networks, comprising: comparing in substantially real-time thequalities of multiple interfaces using independent QoI metrics andmethods used to obtain QoI measurement, whether measured by a paththrough a radio network alone or a path through both a radio network andthrough a wired network.

In some illustrative examples a multi-interface mobile device or hostmay have N interfaces (e.g., where N=2 or more). In some preferredembodiments, measurements are taken on each of these interfaces thatreflect the Quality of Interface (QoI). Illustrative examples of suchmeasurements can include: end-to-end available bandwidth; round-tripdelay; etc. The mobile device or host preferably includes a functionalelement or module with which a comparison of the QoI measurements takesplace.

In the preferred embodiments, methods for quick, real-time comparison ofthe QoIs for different measurement scenarios are provided. Whileillustrative examples are described in which a mobile has twointerfaces, the methods can each be generalized cases when the mobilehas N>2 interfaces.

Case 1

In some examples, we may have many QoI measurements from the currentinterface, but not many from the other interfaces. In such examples, wecan employ a methodology based on quickest sequential change detection.

In the preferred embodiments, the method is related to a cumulative sum(CUSUM) procedure, applied in a novel way as follows: let the nullhypothesis H₀ be that the new interface does not provide significantlyhigher average bandwidth than the current path. We can apply the CUSUMprocedure to solve the path quality comparison problem as follows:

Step 1: From m observations {X₁, . . . , X_(m)} on the currentinterface, compute the sample mean μ₁ and the sample standard deviationσ₁.

Step 2: For each new observation X_(m)

a) If the observation is from the current interface, update μ₁ and σ₁,

b) If X_(m) is from the new interface, calculate

Z_(n)=(Z_(n−1)+((X_(m)−μ₁)/σ₁−k)^+, Z−0=0 where (x)^+=x

if x>0, and 0 otherwise.

Step 3: If Z_(n)>h, then recommend switching interfaces (reject H₀),otherwise don't switch interface.

Note that the above procedure treats the observations from the old thenew interfaces as if they are from the same data series and then seeksto detect whether and when the mean of this combined data series willchange. This type of approach for detecting changes in a mean fall intothe genre of methods commonly referred to as one-sample change-pointdetection methods.

An example of this process is shown in FIG. 2. An interface switch isinitiated/recommended after the 12th observation, indicated by the redpoints above the threshold line h.

Case 2

Suppose we have paired observations, (X_(n),Y_(n)), one observation foreach interface. If necessary, we construct paired observations bydiscarding the “old” observations on the current interface that do nothave matching observations on the new interface and then match eachobservation on the new interface by its closest observation in time onthe current interface.

Paired observations can be used for effective path quality comparisons,for example, when the path quality metric is highly non-stationary, orX_(n) and Y_(n) are strongly correlated, or when paired observations onboth interfaces are easy to obtain. Since each interface uses adifferent network path, X_(n) and Y_(n) are assumed to be independent.We consider the differences: Xt_(n)=X_(n)−Y_(n).

If the scale of the Xt_(n) is known/estimated, e.g., σt₁, we proceed bycalculatingZt _(n)=((Zt _(n−1)+(Xt _(n) /σt ₁ −kt),Zt 0=0.as in Step 2b) described above, and rejecting H₀ if in Step 3, forappropriate values of ht and kt.

In the quickest change detection methods 1) and 2) described above,accurate estimates of the mean path quality of the current network pathand/or its variance are needed. In the following alternate approaches,however, such is not needed.

Case 3

In some examples, a mobile may not have extensive knowledge of thequality of the current path but may have several measurements for bothpaths. In such a context, it is still possible to make a principleddecision as to which interface has a higher quality.

In the preferred embodiments, a two-sample statistical test is used tocompare the quality of the two paths. The input data to the test are themeasured or predicted available bandwidth values for each interface. Aseach interface uses a different network path, we assume the data pointsfor the two interfaces are independent samples. We test the differencein average quality by testing for a difference in location (mean/median)of the two samples. Our approach includes using a test which isnon-parametric (distribution free), works for unequal sample sizes, andis online (quick to compute, essentially independent of the length ofthe available samples.). A test which satisfies these requirements isRosenbaum's test. Let A={X₁ . . . , X_(n1)} denote the set ofobservations of available bandwidth included in sample set A containingsamples from the current interface. Here, X₁ . . . X_(n1) are assumed tofollow an arbitrary distribution with cumulative distribution function,F(x). . . . Similarly, let B={Y₁ . . . Y_(n2)} denote the values of theobservations from the new interface, with common c.d.f. G(x). Letn=n1+n2. Without loss of generality, let the location of G(x) be Thetaand the location of F(x) be 0. The null-hypothesis of our tests is: H₀:Theta=0 (i.e., the old interface does not provide significantly higheraverage available bandwidth), which is tested against the alternativeH₁: Theta >0.

Suppose the spread of a distribution increases with its location and saythe location of sample set B is greater than the location of samples inA. Suppose we combine sample sets A and B to create one large orderedsample set. Then, there is a tendency for B's data points to accumulateat the high end of the combined sample if the new interface provideshigher average available bandwidth. Keeping this in mind, Rosenbaum'stest statistic T_(R) involves the total number of B's data points, whichhave a value larger than the largest value in A.

If the overall maximum is not from sample set B, then the test stopsimmediately, and the null-hypothesis H₀ is not rejected in favor of H₁.Otherwise, H₀ is rejected in favor of H₁ if T_(R) is larger than acritical value C. To calculate C, we use the distribution of T_(R). Forindependent samples this is easy, for any sample size n=n1+n2.Asymptotically as n1→∞, n2→∞, with n2/N 1→p, this converges to simply:P(T_(R)>=h)=≈p^h.

Note that the Rosenbaum test is based on the length of the high-end“extreme run” of the samples in B, which is easy to track online.

This simple test is well-suited for cases where an increase in the meanis accompanied by an increase in the variance. Similarly, if we wouldhave prior knowledge that typically a high location (e.g., mean ormedian) would be accompanied by a low spread (e.g., variance), then wecould construct a similar test which only considers the “low-end”extreme run of the samples in A. This would be equally easy to apply.

On-line application of the above tests implies repetition of the testfor every new observation or every new batch of observations. This meansthat the critical values of the tests would preferably be adjustedand/or that the tests are only applied a finite number of times.

Case 4

In some embodiments, if we have paired observations (X_(n), Y_(n)),which are strongly correlated, the two sample tests described in Case 3above are weaker or even invalid. To overcome this, we can usenon-parametric approaches to test the differences. One on-linenonparametric test that we used for this purpose is the Wilcoxonsign-test. A similar non-parametric test, the Wilcoxon signed-rank testis more powerful, but has to maintain ranked samples, hence it is not anon-line algorithm.

Various Embodiments

Among other things, the preferred embodiments do not depend onapplication specific (e.g. decibel) thresholds. As expressed above, inthe preferred embodiments, the cumulative sum tests (see 1 and 2) areused in a novel way, to compare two different samples, instead ofdetecting a change-of-mean in one sample or two-sample tests in (see 3and 4) are chosen to be non-parametric (distribution free) andimplementable in an on-line manner. Among other things, this is incontrast to parametric tests, which typically make the assumption thatunderlying distributions are Gaussian, and then use asymptoticallyoptimal likelihood-ratio based tests, and traditional optimalnonparametric tests, which typically require availability of the entiremeasurement history. Moreover, the tests have good small samplebehavior.

When a mobile has N>2 interfaces available, the above-discussedcomparison tests can still be used. In this regard, a decision ofinterest is the best network path among the N candidates; accordingly, atotal of N−1 pair-wise tests (e.g., between the current path and each ofthe N−1 alternatives) can be employed in some embodiments.

According to the preferred embodiments, the solutions are not limited toradio signal strength measurements, and can take into account, interalia, the quality of the end-to-end path from an interface to acorresponding host. In contrast, current approaches for mobile interfacecomparison only take into account the radio network, and in particularare largely based on signal strength alone.

The preferred embodiments described herein solve existing problems inthe art based on a unique understanding of computer networks,performance measurements, statistics and more. Existing methods thatinvolve merely measuring bandwidth are not suitable for wirelessnetworks. In addition, other art single-handedly addresses comparingmetrics but those arts require information for the comparison which maynot be available to mobile hosts. Similarly, statistical changedetection methods have been devised which typically only looks forchanges in single measurement streams instead of multiple ones.

The above and/or other aspects, features and/or advantages of variousembodiments will be further appreciated in view of the followingdescription in conjunction with the accompanying figures. Variousembodiments can include and/or exclude different aspects, featuresand/or advantages where applicable. In addition, various embodiments cancombine one or more aspect or feature of other embodiments whereapplicable. The descriptions of aspects, features and/or advantages ofparticular embodiments should not be construed as limiting otherembodiments or the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiments of the present invention are shown by a way ofexample, and not limitation, in the accompanying figures, in which:

FIG. 1(A) is an architectural diagram depicting an illustrativeenvironment in which a mobile device having a plurality of interfacescommunicates with a plurality of networks;

FIG. 1(B) is another architectural diagram depicting an illustrativeenvironment in which a mobile device having a plurality of interfacescommunicates with a plurality of networks, and also illustrating somecomponents of an illustrative mobile device and an illustrative accesspoint with which the mobile device communicates in a first network;

FIG. 2(A) is a graph depicting Ethernet and Wavelan available pathbandwidths to an illustrative node at www.toast.net;

FIG. 2(B) is a set of graphs showing pair-wise correlation functionsbetween available bandwidth series of Ethernet and Wireless linksaccording to some illustrative examples;

FIG. 3 is a graph showing illustrative results for a CUSUM test on realmeasurements according to some illustrative examples;

FIG. 4 is an illustrative graph showing Ethernet, Wavelan and DCMA 20001×RTT available path bandwidths;

FIG. 5 is illustrative pair-wise correlation functions between theavailable bandwidth series of the three links;

FIG. 6 is an illustrative graph showing results for CUSUM test on realmeasurements; and

FIG. 7 is an illustrative graph showing results for CUSUM matched pairtest on real measurements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

While the present invention may be embodied in many different forms, anumber of illustrative embodiments are described herein with theunderstanding that the present disclosure is to be considered asproviding examples of the principles of the various inventions describedherein and that such examples are not intended to limit the invention topreferred embodiments described herein and/or illustrated herein

General:

Emerging mobile devices are equipped with multiple interfaces allowingusers to take advantage of heterogeneous radio technologies (e.g.,cellular networks and wireless LANs). To support mobility and quality ofservice (QoS) for such multi-interface mobile devices, it is importantto dynamically determine which interface or interfaces a mobile shoulduse and when to switch from one interface to another. To make suchdecisions, it is critical for the mobile to compare the “qualities ofthe interfaces (QoI)”, which may be measured by a variety of metrics, inreal time so that the mobile can switch from one interface to anotherwithout user perceivable interruptions to on-going applications. Thisapplication, among other things, presents and analyzes new techniquesfor a mobile device to make rapid comparison of the quality of theinterfaces using quickest sequential change detection and sequentialtwo-sample hypotheses tests.

The preferred embodiments contemplates that future wireless networkswill use multiple radio technologies to meet different communicationneeds. For example, short-range radio technologies (e.g., Bluetooth) maybe used to connect nearby devices (e.g., devices carried by a person orinside an office, vehicle, or home); private or public wireless LANs(e.g., IEEE 802.11) for continuous coverage throughout a building, acampus, an airport, a shopping mall, and cellular radio systems (e.g.,GSM, GPRS, cdma2000, WCDMA) for wide-area coverage. Therefore, mobiledevices (also referred to herein as mobiles) are being equipped withmultiple radio interfaces, each supporting a different radio technology.

By way of example, FIG. 1(A) shows an illustrative future networkconfiguration in which a mobile device 1 is shown as having interfacesfor communicating with base stations 2A, 2B and access points 3A, 3B. Inthis illustrative example, the base stations 2A and 2B are shown ascommunicating with a base station controller 4 that in turn communicateswith a call agent 7 which is in communication with the public switchedtelephone network (PSTN) 12. As also shown, the access points 3A and 3Bcan include, e.g., IP network access points and can be in communicationwith a gateway 5 that communicates, in turn to a router 6 thatcommunicates via an IP network 10, such as, e.g., the Internet, via atrunking gateway 11 to the public switched telephone network 12.

To take full advantage of different radio technologies, a mobile devicepreferably is configured to select which radio interfaces (i.e., radionetworks) to use and when to switch between the interfaces (hence,between radio networks), preferably in a seamless manner with littleinterruption to user applications. Therefore, a significant question toanswer before switching to a new interface is whether a new interfacewill provide higher quality for a future time period than the currentlyused interface. The quality of an interface (QoI) can be measured indifferent ways depending on circumstances, such as, e.g., the needs ofuser applications.

With reference to FIG. 1(B), in some illustrative embodiments a mobiledevice 1 can include a plurality of interfaces. In the illustratedembodiment, three interfaces are shown: Interface 1; Interface 2 andInterface 3. However, in various embodiments any number of interfacescan be employed. In illustrative embodiments, a mobile device caninclude, e.g., portable computers, personal desk-top computers, PDAs,portable voice-over-IP telephones and/or other devices. Typically, suchmobile devices will include a transceiver (including an antenna forcommunication with the access point), a processor, memory (including,e.g., program memory and Random Access Memory). As also shown, thememory can include a program or module, such as, e.g., QoI ComparisonModule for carrying out functionality as described herein-below. Invarious embodiments, processes to be carried out by the mobile devicecan be performed via software, hardware and/or firmware as may beappropriate based on circumstances.

In an illustrative embodiment shown in FIG. 1(B), a mobile device 1 isshown that is capable of communicating via a plurality of networks, suchas, e.g., via Interface 1-3. For example, the mobile device cancommunicate via the Access Point 22 or via a Base Station 2, similar tothat shown in FIG. 1(B). Additionally, FIG. 1(B) also schematicallydepicts an example in which the mobile device 1 can also communicatewith another network, such as, e.g., another wireless network or a wirednetworks. With reference to FIG. 1(B), in some illustrative andnon-limiting embodiments, the access point 22 can be within a wirelesslocal area network (WLAN) connected to a wireline network 20. In someexamples, the wireline network 20 can include the Internet or acorporate data processing network. In some examples, the access point 22can be a wireless router. In some embodiments, the access point 22 canhave a network interface 25 linked to the wireline network 21 and awireless transceiver in communication with the mobile device 1 and withother mobile devices. By way of example, the wireless transceiver 26 caninclude an antenna 27 for radio or microwave frequency communicationwith the mobile devices. The access point 22 preferably also has aprocessor 28, a program memory 29, and a random access memory 31.

Today, mobile devices typically use only the information regarding radionetworks to measure and compare QoI. This information includes radiosignal strength or signal-to-noise ratio (SNR). However, a high signalstrength or a high signal-to-noise ratio does not necessarily mean ahigh available bandwidth or a low delay through the radio network.

In general, the QoI should reflect the quality of the network path,which may involve a path through the radio network and a path through awired backbone network (such as, by way of example, a wireline network20 like that shown in FIG. 1(B)), between the mobile device interfaceand a corresponding node with which, for example, the mobile device isultimately communication with. Depending on circumstances, the qualityof a network path may be measured in a variety of ways includingunidirectional delay, round-trip delay, delay jitter, availablebandwidth of the network path, or their combinations.

In some instances, the network path used to measure the QoI can be thepath through the radio network only, for instance when the radio networkis known to be the bottleneck in the end-to-end path. However, it isoften difficult for a mobile to know whether a radio network or a wiredbackbone network will be the bottleneck for a particular applicationbecause it depends on a large number of factors that can dynamicallychange over time. These factors include, for example, the specificdesigns of the radio and the backbone networks, the current networkloads in the radio and the backbone networks, the specific requirementsof the application, and even the location of the mobile device.

Therefore, it is advantageous for a multi-interface mobile to be able tocompare in real time the qualities of multiple interfaces regardless ofhow the QoI is measured—e.g., whether it is measured by the path throughthe radio network alone or by an end-to-end path that includes both apath in the radio network and a path through a wired network.

In this disclosure, novel and advantageous solutions to this QoIcomparison problem are presented in which, for example, the availablebandwidth of a network path is used as the measure of QoI. Accordingly,we will now discuss in more detail the meaning of the availablebandwidth of a network path. To begin with, we first discuss thecapacity of a network path. As discussed in Reference [10] incorporatedabove, the capacity of a link along a network path can be defined as themaximum possible IP-layer transfer rate across that link. Accordingly,the capacity C of the network path can then defined as the minimum linkcapacity in the path:C=min_(i=1, . . . , H)C_(i),where C_(i) is the capacity of the i-th link along the path and H is thenumber of links in the path. The available bandwidth of a link is theunused, “spare” capacity of the link during a certain time period. IfC_(i) is the capacity of link i, and u is the average utilization ofthat link in the given time interval, then the average availablebandwidth A_(i) of link I will be:A _(i)=(1−u _(i))C _(i)The available bandwidth of an H hop network path is then the minimumavailable bandwidth among the H links:A=min_(i=1, . . . ,H)A_(i).

Illustrative approaches for estimating available path bandwidth existand can be broadly classified in the following categories:

Self-Induced Congestion Approaches.

These approaches measure the available bandwidth or “spare” capacityalong a network path. Self-induced congestion approaches are based onthe following principle: If the rate of the probe packets exceeds theavailable bandwidth over a path, congestion will occur and the probepackets will be queued at the bottleneck router (or routers) along thepath causing the inter-packet time interval to be longer at the receiverthan at the sender. On the other hand, if the probing rate is below theavailable bandwidth over the path, no congestion will occur and theprobe packets will experience no queuing delay. Therefore, the availablebandwidth along a path can be estimated as the probing rate at the onsetof congestion.

A few examples of self-induced congestion approaches include Pathloaddiscussed in Reference [2] incorporated above, TOPP discussed inReference [3] incorporated above, and PathChirp discussed in Reference[1] incorporated above. These methods need significant cooperationbetween nodes at both ends of a path to estimate the available pathbandwidth.

Packet Pair Train Dispersion Methods.

With reference to References [4]. [5] and [11], packet pair traindispersion methods estimate the Asymptotic Dispersion Rate along a path,a measure related to capacity, but influenced by the amount of crosstraffic. Packet pair/train dispersion techniques typically needmeasurements at both ends of the network path. However, it is alsopossible to perform these measurements without access at the receivingnode, by forcing the receiving node to send a message (such as, e.g., byforcing the receiving node to send ICMP port-unreachable or TCP RSTpackets) in response to each probe packet (see Reference [11]incorporated above).

Combined Self-Induced Congestion and Packet Pair Approaches.

In some embodiments, it is also possible to successfully combineself-induced congestion and a packet pair technique: examples of suchhybrid methods include IGI, and PTR (see Reference [5] incorporatedabove). Like pathChirp, these hybrid methods are relatively efficient,specifically compared to the Pathload method. PathChirp reportedly usesless 10% of the probe-volume of the Pathload method (see Reference [1]incorporated above), and IGI/PTR converges much faster than the Pathloadmethod, in approximately 10% of the time (see Reference [5] incorporatedabove). The reported convergence time of IGI/PTR is on the order of 4-6round-trip times (RTTs).

TCP Bulk File Transfer Rate Approaches.

These methods measure available bandwidth by measuring the transfer timeof a fixed-size file using the TCP protocol. A benefit of this techniqueis that it more closely reflects the available path bandwidth to beexperienced by a data-application. However, the quality of theestimation depends on the behavior of the TCP protocol which may differdepending on the type of networks. If the file is requested or pushedfrom a server (e.g., a Web server), the quality of the estimation canalso be affected by the load on the server. Since these methods use aTCP connection between both end nodes, they implicitly requirecooperation at both ends of the network path.

A straight-forward approach to comparing the average available pathbandwidths of two network paths over a future time period is to firstestimate the average available path bandwidths probing mobile device to,send out a large number of probe packets. For example, Pathload (seeReference [2] incorporated above) may require several megabytes ofprobing traffic and may take hundreds of RTTs to obtain an estimate ofthe available bandwidth over a path. Although the packet pair approachescan reduce the number of probe packets, a substantial number of probepacket pairs will still be needed to obtain each sample of availablebandwidth in order to reduce the potential inaccuracy caused by crosstraffic. Sending a large number of probe packets will waste the scarcepower resources on the mobiles and the scarce radio bandwidth and mayalso result in excessive handoff delay.

Methods also exist that rely on the knowledge of the past and recentlink utilizations to estimate available bandwidth and to then selectroutes or paths to connect the traffic sources and destinations (seeReferences [6] and [7] incorporated above). They are in fact used in acontext of a single autonomous domain, where the network operatortypically has access to the required statistics. However, thesestatistics are hard to know or estimate for an individual mobile, andtherefore these methods are not suitable to be used by a mobile tocompare the qualities of its multiple interfaces in real time.Therefore, in the preferred embodiments, a new category of methods thatcan be used by a multi-interface mobile device is presented in order tocompare in substantially real-time or in real-time the qualities of itsinterfaces regardless of what QoI metrics is used (e.g., be it by theinformation regarding the radio network alone or by the availablebandwidth of an end-to-end path) and how the QoI measurement isobtained.

The preferred embodiments are preferably adapted to meet the followingrequirements for QoI comparison needed by mobile terminals:

-   -   Minimizing the probing traffic to be sent over the interfaces to        reduce the consumption of the scarce power resources on the        mobile and the scarce radio network bandwidth for transporting        the probe traffic.    -   Performing the comparison as quickly as possible to reduce        handoff delay and potential interruptions to user applications.    -   Supporting the comparison of both short-term and long-term QoI        because the QoI may vary widely over time.    -   Being able to handle arbitrary probability distributions of the        QoI and the fact that this probability distribution may not be        known to the mobile when QoI comparison needs to be performed.

The following section under Exemplary Embodiments sets forth and furtherdescribes some proposed approaches, followed by a discussion ofillustrative performance results of the proposed approaches based onavailable path bandwidths measured over multiple real networks.

Exemplary Embodiments

1. Basic Approach

Consider a mobile terminal with two interfaces, for example, onecellular radio interface allowing the mobile to access GSM, GPRS,cdma2000, or WCDMA and one wireless local area network (LAN) interfacesuch as IEEE 802.11 allowing the user to access his/her enterprisenetworks and public wireless LAN hotspots. At any given time, we referto the interface that the mobile has been using to support userapplications as the current or old interface. We call the network pathbetween the old interface and a reference node the old network path. Thereference node could be, for example, the corresponding node or agateway that the traffic between the mobile and the corresponding nodetraverses. We refer to the interface that the mobile is not currentlyusing to transport application traffic as the new interface and we callthe network path between the new interface and the reference node thenew network path.

One objective of the QoI comparison problem is to determine in real timewhether the new interface will have a significantly higher QoI (which,we assume as an example, is measured by the average available pathbandwidth.) over a future time period. To meet the main designrequirements outlined above, a central idea is to take advantage of theinformation a mobile can accumulate on the QoI of the old interface in away that the mobile needs little information on the QoI of the newinterface to carry out the comparison. This idea allows the proposedmethods to have the following important characteristics:

Able to compare the average available path bandwidth without having toknow the average path bandwidth of a new interface first. The preferredembodiments do not require the mobile to know the average availablebandwidth of the new path first in order to compare it with the averageavailable bandwidth of the old path. The number of samples of theavailable bandwidth over the new path needed by the preferredembodiments can be significantly smaller than the number of samplesneeded to estimate the average available bandwidth of the new path. Infact, in some embodiments, a single sample could be used to obtain arough comparison. In other embodiments, more samples could help increasethe accuracy of the comparison. As a result, the preferred embodimentscan significantly reduce the number of probe packets to be sent over thenew path and the time it takes to complete the bandwidth comparison.

Independent of QoI metrics and methods used to obtain QoI measurement.The preferred embodiments involve methodologies that apply to anymetrics used to measure QoI—such as, e.g., the available bandwidth of anend-to-end path or the path inside a radio network only, average delay,and average signal strength or SNR. Furthermore, the preferredembodiments involve approaches that do not depend on any particularmethod for obtaining samples of the QoI. For example, when the availablepath bandwidth is used to measure QoI, any methods described above forestimating the available path bandwidth may be used to obtain samples ofthe available path bandwidth.

Able to meet preset target confidence levels. The proposed methods arecapable of performing comparisons that meet given confidence levels,i.e., limits on the occurrence of “false alarms”.

2. Approaches Based on Quickest Sequential Change Detection

We assume that the mobile takes samples (observations) of the availablebandwidth of the path it has been using to estimate the distribution ofthe available bandwidth on this path. This way, the mobile can obtain agood estimate of the average available path bandwidth μ₁ of the old pathand its standard deviation σ₁. We formulate the path quality comparisonproblem as a quickest change detection problem for observations of theavailable bandwidth from the new interface.

Suppose we have observations B={Y₁, Y₂, . . . } from the new interface.The interface quality comparison problem is then to detect whether andwhen the mean of these samples in B will be higher than μ₁. In otherwords, if from the m-th sample, the sample mean starts to be larger thanσ₁, we want to detect this change in the mean as quickly as possible.

The quickest sequential detection problem is discussed in Reference [8]incorporated herein above. The detection procedures typically seek tominimize the average detection delay T=N−m+1 (if m<∞) (where m is thetime the mean-change occurred, and N is the time at which this change isdetected), subject to a bound on the average run length or false alarmprobability.

Next, we describe two ways to use sequential change detection methods inorder to compare the path qualities. The first approach assumes that theobservations from the old the new interfaces are obtained at differenttimes, i.e., the observations cannot be well matched in time. This, forexample, will be the case when observations of the old path are takenmostly before the need for a handoff occurs and the observations fromthe new path are taken at and immediately after the time point themobile initiates the path quality comparison procedure (i.e., during thehandoff process). The second approach assumes that the observations fromthe old’ and new interfaces can be matched in time. This, for example,will be the case when the mobile can obtain observations from bothinterfaces at approximately the same time points.

Sequential Change Detection With Unmatched Observations

An efficient detection procedure, which has been proved optimal in thecase of i.i.d., normally distributed observations, and in a variety ofother settings (see Reference [9] incorporated above), is Page'scumulative sum or CUSUM procedure. It defines the variables

${{\overset{\sim}{Y}}_{i} = {\frac{Y_{i} - {\hat{\mu}}_{1}}{{\hat{\sigma}}_{1}} - k}},$where k is a design parameter. Furthermore, it defines the variablesW _(n) =S _(n)−min_(0≦i≦n) {S _(i) },n≧1whereS_(i)=Σ_(j=1) ^(i)Y_(j), with W₀=0The test now declares that a change has occurred as soon as W_(n)≧h,h>0.

A CUSUM procedure is defined by the two parameters k and h (seeReference [16] incorporated above). They are typically chosen to makethe Average Run Length (ARL) of the test large if there is no change inthe process mean, and small if there is a significant change in themean. Parameter k influences the minimum (quickly) detected change, andkeeps the test statistic small when there is no change in the mean. Wecan think of k as the overhead cost of switching interfaces. ARL valuesfor different values of k and h have been tabulated and these tables canbe used for selecting the values of parameters k and h.

Let the null hypothesis H₀ be that the new interface does not providesignificantly higher average bandwidth than the current path, then wecan apply the CUSUM procedure to solve the path quality comparisonproblem as follows:

Step 1: From m observations{X₁, . . . , X_(m)}on the current interface, compute the sample mean{circumflex over (μ)}₁and the sample standard deviation{circumflex over (σ)}₁

Step 2: For each new observation X_(n):

If the observation is from the current interface, update{circumflex over (μ)}₁and{circumflex over (σ)}₁

If X_(n) is from the new interface, calculate

${Z_{n} = \left( {Z_{n - 1} + \frac{X_{n} - {\hat{\mu}}_{1}}{{\hat{\sigma}}_{1}} - k} \right)^{+}},{Z_{0} = 0}$where (x)⁺=x if x>0, and 0 otherwise.

Step 3: If Z_(n)>h

then switch interfaces (reject H_(o)), otherwise don't switch interface.

Note that the above procedure treats the observations from the old thenew interfaces as if they are from the same data series and then seeksto detect whether and when the mean of this combined data series willchange. Such approaches for detecting changes in the mean are commonlyreferred to as one-sample change-point detection methods.

Matched Observations: Instantaneous Comparison

Now, suppose we have paired observations—e.g., the observations come inpairs, (X_(n), Y_(n)), one for each interlace. If necessary, weconstruct paired observations by discarding the “old” observations onthe current interface that do not have matching observations on the newinterface and then match each observation Y_(n) on the new interface byits closest observation in time X_(n) on the current interface.

Paired observations can be used for effective path quality comparisons,for example, when the path quality metric is highly non-stationary, orX_(n) and Y_(n) are strongly correlated, or when paired observations onboth interfaces are easy to obtain.

Since each interface uses a different network path, X_(n) and Y_(n) areassumed to be independent. We consider the differences:{tilde over (X)} _(n) =X _(n) −Y _(n)

If the scale of the X_(n) is known, e.g.,{tilde over (σ)}₁

Then, we proceed by calculating

${{\overset{\sim}{Z}}_{n} = \left( {{\overset{\sim}{Z}}_{n - 1} + \frac{{\overset{\sim}{X}}_{n}}{{\overset{\sim}{\sigma}}_{1}} - \overset{\sim}{k}} \right)^{+}},{{\overset{\sim}{Z}}_{0} = 0}$as in Step 2b) described above, and rejecting H_(o) if{tilde over (Z)}_(n)>{tilde over (h)}in Step 3, for appropriate values of{tilde over (h)} and {tilde over (k)}

As for the limitations of the quickest change detection methodsdescribed above: a requirement for application of the above methods, isthat we have accurate estimates of the mean path quality of the currentnetwork path and/or its variance.

Next, an approach is presented which is based on two-sample statisticaltests, for cases where we do not have extensive knowledge of the qualityof the current path.

3. Approaches Based on Sequential Two-Sample Tests

Suppose now that we do not have extensive knowledge of the quality ofthe current path. For example, we do not have sufficiently manymeasurements to have a reliable estimate of the mean quality of thecurrent path and its variance, and we do not have matched observations.In that case, we cannot apply the methods described above underApproaches Based on Quickest Sequential Change Detection. However,suppose we do have several measurements for both paths. Then, we may usea two-sample statistical test to compare the quality of the two paths.Without knowing the exact values of the mean/median, we can still do atest of which interface has the higher mean or median. The input data tothe test are the measured or predicted available bandwidth values foreach interface. As each interface uses a different network path, weassume the data points for the two interfaces are independent samples.

We test the difference in average quality by testing for a difference inlocation (mean/median) of the two samples. The test and its teststatistic should ideally have the following properties:

1) It should be non-parametric, preferably distribution free, as amobile usually does not know the precise statistical distribution of thequality indicator, e.g. available bandwidth.

2) It should work for unequal sample sizes. Note that we may have manymeasurements on one (the current) interface, and only a few on the otherinterface due to time and mobility concerns.

3) It should be online, that is: quick to compute, and essentiallyindependent of the length of the available samples. In particular, we donot want to keep around all past data values.

One parametric test, which possesses property 2 and 3 is the two samplet-test. A non-parametric test, which has property 1 and 2 is theMann-Whitney test (see Reference [9] incorporated above). One simpleprocedure, which possesses (approximately) properties 1, 2 and 3 isRosenbaum's test (see Reference [9] incorporated above). We describethis test in more detail next.

Let A={X₁, . . . , X_(n1)} denote the set of n₁, observations ofavailable bandwidth included in sample set A containing samples from thecurrent interface. X₁, . . . , X_(n1) are assumed to follow an arbitrarydistribution with cumulative distribution function F(x). Similarly, letB={Y₁, . . . , Y_(n2)} denote the values of the observations from thenew interface, with common c.d.f. G(x). Let n=n₁+n₂. Without loss ofgenerality, let the location of G(x) be ⊖ and the location of F(x) be 0.The null-hypothesis of our tests is: Ho: ⊖=0 (i.e., the old interfacedoes not provide significantly higher average available bandwidth),which is tested against the alternative H₁: ⊖>0.

Suppose the location (median/mean) of sample set B is greater than thelocation of samples in A. Furthermore, assume that the spread (variance,range) of a sample is greater when its location is greater. Suppose wecombine sample sets A and B to create one large ordered sample set.Then, there is a tendency for B's data points to accumulate at the highend of the combined sample if the new interface provides higher averageavailable bandwidth. Keeping this in mind, Rosenbaum's test statisticT_(R) involves the total number of B's data points, which have a valuelarger than the largest value in A.

If the overall maximum is not from sample set B, then the test stopsimmediately, and the null-hypothesis H₀ is not rejected in favor of H₁.Otherwise, H₀ is rejected in favor of H₁ if T_(R) is larger than acritical value C(α₁,n₁,n₂). To calculate C(α,n₁,n₂), we use thedistribution of T_(R). For independent samples this is easy, for anysample size N=n₁+n₂:

${P\;\left( {T_{R} \geq h} \right)} = {{\frac{n_{2}}{N} \times \frac{n_{2} - 1}{N} \times \ldots \times \frac{n_{2} - h + 1}{N - h + 1}} = \frac{{n_{2}!}{\left( {N - h} \right)!}}{{n!}{\left( {n_{1} - h} \right)!}}}$

Now, asymptotically as n₁→∞, n₂→∞ with

$\left. \frac{n_{2}}{N}\rightarrow p \right.,$this converges to simply:P(T _(R) ≧h)≈p ^(h)

Note that the Rosenbaum test is based on the length of the high-end‘extreme run’ of the samples in B, which is easy to track online. Thissimple test is well suited for the case where an increase in the mean isaccompanied by an increase in the variance. Similarly, if we would haveprior knowledge that typically a high location (e.g., mean or median)would be accompanied by a low spread (e.g., variance), then we couldconstruct a similar test which only considers the “low-end” extreme runof the samples in A. This would be equally easy to apply.

On-line application of the above tests implies repetition of the testfor every new observation or every new batch of observations. This meansthat the critical values of the tests have to be adjusted (see Chapter 7in Reference [8] incorporated above) and/or the tests are only applied afinite number of times. If we have paired observations (X_(n), Y_(n)),which are strongly correlated, the above two-sample tests are weaker oreven invalid. To overcome this problem, we can use nonparametricapproaches to test the differences{tilde over (X)} _(n) =X _(n) −Y _(n)One on-line nonparametric test that can be used for this purpose is theWilcoxon sign-test (see Reference [13] incorporated above). A similarnon-parametric test, the Wilcoxon signed-rank test is more powerful, buthas to maintain ranked samples, hence it is not an on-line algorithm.

When a mobile has N>2 interfaces available, we can still use thetwo-sample comparison tests described in the above sections onApproaches Based on Quickest Sequential Change Detection and ApproachesBased on Sequential Two Sample Tests. Since we are only interested inthe best network path among the N candidates, a total of N−1 pair-wisetests (e.g. between the current path and each of the N−1 alternatives)will suffice.

Performance Analysis:

We have analyzed the performance of our methodology using both simulatedand real measurements of path bandwidths. Due to space restrictions,only the results based on real measurements are presented below.

Results on Real Available Bandwidth Measurements: Ethernet, WLAN, and1×RTT CDMA

We have collected a series of measurements of available bandwidth fromtwo interfaces on a mobile terminal: a 10 Mbps Ethernet link and aWavelan 802.11 11 Mbps link to a test site identified by www.toast.net.The available bandwidth was measured via the TCP bulk file transfertechnique. The available bandwidths were collected every 2 minutes from10:00 AM to 10:56 AM on a particular day on our local Ethernet and WLAN.See FIG. 2(A).

First, we present some preliminary analysis of each of the measurementseries. The Ethernet link has mean deviation 626.4 kbps. As shown inFIG. 2(B), the auto-correlation function plots show both series appearserially uncorrelated. However, the sample correlation coefficientbetween the two series is non-zero, 0.247, indicating that the availablepath bandwidths are not completely independent. Next, we tested thesamples for normality. The Kolmogorov-Smirnov test applied to eachmeasurement series did not reject the hypothesis that they were Gaussianwith their given mean and standard deviation. The Shapiro-Wilk test gavethe same results.

Next, we applied the proposed path quality comparison approaches basedon the two CUSUM tests described in the above sections under “ProposedApproaches” to compare the average available bandwidth of the twonetwork paths. The parameters h and k were chosen as follows: 1) k=0.5to achieve good performance for detecting shifts of about one standarddeviation or more, and 2) h=3, corresponding to an Average Run Length ofabout 117 if there is no change in the mean. The first CUSUM test wasrun with “prior knowledge” of{circumflex over (μ)}₁=1126_(kbps. and {circumflex over (σ)})₁391_(kbps).It detects the difference in mean available bandwidth at the 13thobservation, as shown in FIG. 3. Notice that the detection is in factbased on observations 11-13 only. The matched-pair test, with “priorknowledge” of{tilde over (σ)}₁=552.4_(kbps).detects the difference at the 14th observation.

The two-sample tests gave the following results. Listed in Table 1 beloware the p-values for the t-test, the Mann-Whitney test (labeled as MW),and the Rosenbaum test (labeled as Rb) based on 10, 15, 20, 25 and 29(=whole sample) pairs of measurements, respectively. The p-value is theprobability of observing the same or a higher test-statistic value thanthe one obtained, if the null-hypothesis is true, i-e., if there is nochange in the means (or no change in the medians for non-parametrictests that test for a change in the median).

TABLE 1 p-values for two-sample tests for given number of observedmeasurement pairs Test 10 15 20 25 29 t-test 0.534 0.112 0.212 0.0540.012 MW 0.529 0.116 0.192 0.054 0.015 Rb 0.25 0.031 0.25 0.063 0.016

We see that the hypothesis of equal means is not rejected at the 0.05level of significance by the t-test or the Mann-Whitney test, exceptwhen the whole sample is considered. The Rosenbaum test, however,already rejects at the 0.05 level of significance after 15 observations.This is encouraging, since this test does not rely on any priorknowledge, is on-line and the measurement series are weaklycross-correlated.

In light of the non-zero cross-correlation between the measurements fromthe two interfaces, it is interesting to see how the matched-pair testsperform. The results for the Wilcoxon signed-rank test and the sign testare shown below in Table 2.

TABLE 2 p-values for matched-pair tests for given number of observedmeasurement pairs Test 10 15 20 25 29 Wsr 0.557 0.055 0.143 0.022 0.004sign 0.344 0.035 0.041 0.015 0.002

We see that the simple sign-test performs well and seems to be a goodchoice for online implementation on matched pairs of measurements.

Next, we have collected 3 new series of measurements, from threedifferent interfaces on a mobile terminal: an 11 Mbps Wavelan 802.11link, a 10 Mbps Ethernet link and a I×RTT CDMA cellular link, to a testsite identified by www.toast.net. The available bandwidths werecollected every 2 minutes from 10 AM to 5 PM on a particular day on ourlocal WLAN, Ethernet and public cellular network. See FIG. 4.Preliminary analysis of each of the measurement series show, that theWiFi link has mean available bandwidth 1797.6±906.8 kbps, the Ethernetlink has mean available bandwidth 1847.7±860.4 kbps, and the cellularlink has mean available bandwidth 116.0±14.4 kbps.

FIG. 5 shows the nine pair-wise correlation functions, which indicate nosignificant correlation in the series. Furthermore, thecross-correlation coefficients are small as well, not exceeding 0.095.

From FIG. 4, it can be seen that the cdma2000 1×RTT cellular linkprovides significantly lower bandwidth than the LAN and WLAN links.Comparison of the Ethernet and Wavelan links via the CUSUM test gave thefollowing results: suppose the current path is the Wavelan path; whenthe CUSUM test is run on the measurements from the Ethernet path withthe following prior knowledge about the available bandwidth through thecurrent interface: mean 1797.6 kbps and standard deviation 906.9 kbps,the higher mean bandwidth on the Ethernet path is first detected afterthe 11′h observation, as illustrated in FIG. 6. Notice that thedetection is in fact based on observations 9 through 11 only. Theresults are similar when we apply the CUSUM test for matchedobservations. See FIG. 7. Again, the higher mean bandwidth on theEthernet link is first detected at the 111 h matched observation pair.

Broad Scope of the Invention

In the foregoing examples, the problem of interface comparison wasdiscussed in the illustrative and non-limiting context based onavailable path bandwidth. Among other things, methods were presentedabove that use statistical testing to quickly determine whether a newinterface has significantly higher quality than the currently used one.When accurate knowledge exists of the available bandwidth on the currentpath, methods using CUSUM procedures for quickest detection of adifference in mean bandwidth have shown good results. In the absence ofprior knowledge of path quality over the current path, a simplenon-parametric test provided good results.

While illustrative embodiments of the invention have been describedherein, the present invention is not limited to the various preferredembodiments described herein, but includes any and all embodimentshaving equivalent elements, modifications, omissions, combinations(e.g., of aspects across various embodiments), adaptations and/oralterations as would be appreciated by those in the art based on thepresent disclosure. The limitations in the claims are to be interpretedbroadly based on the language employed in the claims and not limited toexamples described in the present specification or during theprosecution of the application, which examples are to be construed asnon-exclusive. For example, in the present disclosure, the term“preferably” is non-exclusive and means “preferably, but not limitedto.” In this disclosure and during the prosecution of this application,means-plus-function or step-plus-function limitations will only beemployed where for a specific claim limitation all of the followingconditions are present in that limitation: a) “means for” or “step for”is expressly recited; b) a corresponding function is expressly recited;and c) structure, material or acts that support that structure are notrecited. In this disclosure and during the prosecution of thisapplication, the terminology “present invention” or “invention” may beused as a reference to one or more aspect within the present disclosure.The language present invention or invention should not be improperlyinterpreted as an identification of criticality, should not beimproperly interpreted as applying across all aspects or embodiments(i.e., it should be understood that the present invention has a numberof aspects and embodiments), and should not be improperly interpreted aslimiting the scope of the application or claims. In this disclosure andduring the prosecution of this application, the terminology “embodiment”can be used to describe any aspect, feature, process or step, anycombination thereof, and/or any portion thereof, etc. In some examples,various embodiments may include overlapping features. In thisdisclosure, the following abbreviated terminology may be employed:“e.g.” which means “for example” and “NB” which means “note well.”

1. A method for substantially real-time comparison ofquality-of-interfaces by mobile devices having multiple heterogeneousinterfaces that communicate over heterogeneous radio networks,comprising: comparing in substantially real-time the qualities ofmultiple interfaces of a mobile device that connect to heterogeneousnetworks using independent QoI metrics and methods used to obtain QoImeasurement, whether measured by a path through a radio network alone ora path through both a radio network and through a wired network, saidcomparing including collecting observation samples of a communicationpath of a currently used interface, and making a value estimate of aquality of said currently used interface based on these collectedsamples; said comparing further including collecting observation samplesof a communication path of a new interface heterogeneous to said currentinterface, and making a value estimate of a quality of said newinterface based on these collected samples; the mobile node determiningif the value estimate of the new interface sufficiently exceeds thevalue estimate of the current interface, and, if so, thereafterrendering a decision to switch communication from said current interfaceto said new interface regardless of whether communication via said firstinterface was sufficient or above an acceptable threshold.
 2. The methodof claim 1, further including performing the comparison in a manner tohandle arbitrary probability distributions of the QoI.
 3. The method ofclaim 1, further including performing the comparison in a manner tohandle probability distributions not known to the mobile devices whenthe QoI comparison is performed.
 4. The method of claim 1, furtherincluding performing the comparison in a manner taking advantage ofinformation a mobile device accumulates on a QoI of an old interface ina manner that the mobile device needs little information on a QoI of thenew interface to carry out the comparison.
 5. The method of claim 4,further including performing the comparison with a number of samplesover the new path significantly smaller than a number of samples neededto estimate an average available metric of the new path.
 6. The methodof claim 1, further including comparing an average available path metricwithout knowing the average path metric of a new interface first.
 7. Themethod of claim 1, further including sending minimal probing traffic formeasuring QoI so as to reduce consumption of power resources on themobile devices and radio network bandwidth.
 8. The method of claim 1,wherein said comparing is performed by the mobile device for selectingbetween a currently used network interface and one of its heterogeneousinterfaces for sending communications, said comparing being performed inreal time during use of said current interface prior to any decision ismade as to any need to switch to a new interface, and further includingsaid mobile device selecting one of said heterogeneous interfaces to usefor communication based on said comparing if a value estimate of the newinterface sufficiently exceeds a value estimate of the current interfaceregardless of whether communication via said first interface issufficient or above an acceptable threshold.
 9. A method forsubstantially real-time comparison of quality of interfaces (QoIs) bymobile devices having multiple heterogeneous interfaces that communicateover heterogeneous wireless networks, comprising: comparing insubstantially real-time the qualities of multiple interfaces of a mobiledevice that connect to heterogeneous networks using path quality metricsthat are independent of how the QoI is measured, whether measured by apath through a wireless network alone or a path through both a wirelessnetwork and through a wired network, said method including comparingpath quality as a quickest change detection problem for observationsfrom a new interface or comparing path quality based on sequential twosample tests; wherein said comparing is performed by the mobile devicefor selecting between a currently used network interface and one of itsheterogeneous interfaces for sending communications, said comparingbeing performed in real time during use of said current interface priorto any decision is made as to any need to switch to a new interface, andfurther including said mobile device selecting one of said heterogeneousinterfaces to use for communication based on said comparing if a valueestimate of the new interface sufficiently exceeds a value estimate ofthe current interface regardless of whether communication via said firstinterface is sufficient or above an acceptable threshold.
 10. The methodof claim 9, wherein said multiple heterogeneous interfaces includemultiple radio interfaces each supporting a different radio technology,including both cellular and non-cellular technologies and wherein saidcomparing includes performing comparisons of concurrent transmissionsover said current interface and said new interface in real time withsuch transmissions.
 11. A method for substantially real-time comparisonof quality of interfaces (QoIs) by mobile devices having multipleheterogeneous interfaces that communicate over heterogeneous wirelessnetworks, comprising: comparing in substantially real-time the qualitiesof multiple interfaces of a mobile device that connect to heterogeneousnetworks using path quality metrics that are independent of how the QoIis measured, whether measured by a path through a wireless network aloneor a path through both a wireless network and through a wired network,said method including performing a one-sample change point detectionmethod or performing a non-parametric and on-line two-sample method;wherein said comparing is performed by the mobile device for selectingbetween a currently used network interface and one of its heterogeneousinterfaces for sending communications, said comparing being performed inreal time during use of said current interface prior to any decision ismade as to any need to switch to a new interface, and further includingsaid mobile device selecting one of said heterogeneous interfaces to usefor communication based on said comparing if a value estimate of the newinterface sufficiently exceeds a value estimate of the current interfaceregardless of whether communication via said first interface issufficient or above an acceptable threshold.
 12. The method of claim 11,wherein said multiple heterogeneous interfaces include multiple radiointerfaces each supporting a different radio technology, including bothcellular and non-cellular technologies and wherein said comparingincludes performing comparisons of concurrent transmissions over saidcurrent interface and said new interface in real time with suchtransmissions.
 13. A method for substantially real-time comparison ofquality of interfaces (QoIs) by mobile devices having multipleheterogeneous interfaces that communicate over heterogeneous wirelessnetworks, comprising: comparing in substantially real-time the qualitiesof multiple interfaces of a mobile device that connect to heterogeneousnetworks using path quality metrics that are independent of how the QoIis measured, including comparing path quality as a quickest changedetection problem for observations from the new interface; wherein saidcomparing is performed by the mobile device for selecting between acurrently used network interface and one of its heterogeneous interfacesfor sending communications, said comparing being performed in real timeduring use of said current interface prior to any decision is made as toany need to switch to a new interface, and further including said mobiledevice selecting one of said heterogeneous interfaces to use forcommunication based on said comparing if a value estimate of the newinterface sufficiently exceeds a value estimate of the current interfaceregardless of whether communication via said first interface issufficient or above an acceptable threshold.
 14. The method of claim 13,wherein said multiple heterogeneous interfaces include multiple radiointerfaces each supporting a different radio technology, including bothcellular and non-cellular technologies and wherein said comparingincludes performing comparisons of concurrent transmissions over saidcurrent interface and said new interface in real time with suchtransmissions.